{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据准备与读入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 模块准备\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "import cufflinks as cf\n",
    "import plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "cf.set_config_file(offline=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 494 entries, 0 to 493\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   排名            494 non-null    int64 \n",
      " 1   企业名称          494 non-null    object\n",
      " 2   Company Name  494 non-null    object\n",
      " 3   估值（亿人民币）      494 non-null    int64 \n",
      " 4   国家            494 non-null    object\n",
      " 5   城市            494 non-null    object\n",
      " 6   行业            494 non-null    object\n",
      " 7   掌门人/创始人       494 non-null    object\n",
      " 8   成立年份          494 non-null    int64 \n",
      " 9   部分投资机构        494 non-null    object\n",
      "dtypes: int64(3), object(7)\n",
      "memory usage: 38.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# 读取数据\n",
    "df = pd.read_csv (\"hurun_unicorn.tsv\", encoding = \"utf8\", sep=\"\\t\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>序号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Infor</td>\n",
       "      <td>Infor</td>\n",
       "      <td>3500</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>云计算</td>\n",
       "      <td>Jim Schaper</td>\n",
       "      <td>2002</td>\n",
       "      <td>Golden Gate Capital, Koch Equity Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>3400</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>消费品</td>\n",
       "      <td>Adam Bowen, James Monsees, Kevin Burns, Tim Da...</td>\n",
       "      <td>2015</td>\n",
       "      <td>M13, Timothy Davis, Evolution VC Partners, Tig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>264</td>\n",
       "      <td>Zeta Global</td>\n",
       "      <td>Zeta Global</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>David A. Steinberg, John Sculley</td>\n",
       "      <td>2007</td>\n",
       "      <td>GPI Capital, GSO Capital Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>264</td>\n",
       "      <td>掌门1对1</td>\n",
       "      <td>Zhangmen</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>教育科技</td>\n",
       "      <td>张翼</td>\n",
       "      <td>2014</td>\n",
       "      <td>顺为资本、达晨创投、华平投资</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>264</td>\n",
       "      <td>转转</td>\n",
       "      <td>Zhuanzhuan</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>姚劲波</td>\n",
       "      <td>2015</td>\n",
       "      <td>腾讯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>264</td>\n",
       "      <td>Zipline International</td>\n",
       "      <td>Zipline International</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>半月湾</td>\n",
       "      <td>物流</td>\n",
       "      <td>Keenan Wyrobek, Keller Rinaudo, Will Hetzler</td>\n",
       "      <td>2014</td>\n",
       "      <td>Sequoia Capital, Visionnaire Ventures, Katalys...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>264</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>Ian Siegel, Joe Edmonds, Ward Poulos, Willis Redd</td>\n",
       "      <td>2010</td>\n",
       "      <td>IVP (Institutional Venture Partners)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>494 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      排名                   企业名称           Company Name  估值（亿人民币）  国家   城市  \\\n",
       "序号                                                                          \n",
       "0      1                   蚂蚁金服          Ant Financial     10000  中国   杭州   \n",
       "1      2                   字节跳动              Bytedance      5000  中国   北京   \n",
       "2      3                   滴滴出行           Didi Chuxing      3600  中国   北京   \n",
       "3      4                  Infor                  Infor      3500  美国   纽约   \n",
       "4      5              JUUL Labs              JUUL Labs      3400  美国  旧金山   \n",
       "..   ...                    ...                    ...       ...  ..  ...   \n",
       "489  264            Zeta Global            Zeta Global        70  美国   纽约   \n",
       "490  264                  掌门1对1               Zhangmen        70  中国   上海   \n",
       "491  264                     转转             Zhuanzhuan        70  中国   北京   \n",
       "492  264  Zipline International  Zipline International        70  美国  半月湾   \n",
       "493  264           ZipRecruiter           ZipRecruiter        70  美国  洛杉矶   \n",
       "\n",
       "        行业                                            掌门人/创始人  成立年份  \\\n",
       "序号                                                                    \n",
       "0     金融科技                                                井贤栋  2014   \n",
       "1    媒体和娱乐                                                张一鸣  2012   \n",
       "2     共享经济                                                 程维  2012   \n",
       "3      云计算                                        Jim Schaper  2002   \n",
       "4      消费品  Adam Bowen, James Monsees, Kevin Burns, Tim Da...  2015   \n",
       "..     ...                                                ...   ...   \n",
       "489   人工智能                   David A. Steinberg, John Sculley  2007   \n",
       "490   教育科技                                                 张翼  2014   \n",
       "491   电子商务                                                姚劲波  2015   \n",
       "492     物流       Keenan Wyrobek, Keller Rinaudo, Will Hetzler  2014   \n",
       "493   电子商务  Ian Siegel, Joe Edmonds, Ward Poulos, Willis Redd  2010   \n",
       "\n",
       "                                                部分投资机构  \n",
       "序号                                                      \n",
       "0                                       春华资本、中投海外、红杉资本  \n",
       "1                                  红杉资本、海纳亚洲、纪源资本、启明创投  \n",
       "2                               腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本  \n",
       "3         Golden Gate Capital, Koch Equity Development  \n",
       "4    M13, Timothy Davis, Evolution VC Partners, Tig...  \n",
       "..                                                 ...  \n",
       "489                  GPI Capital, GSO Capital Partners  \n",
       "490                                     顺为资本、达晨创投、华平投资  \n",
       "491                                                 腾讯  \n",
       "492  Sequoia Capital, Visionnaire Ventures, Katalys...  \n",
       "493               IVP (Institutional Venture Partners)  \n",
       "\n",
       "[494 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#设置index\n",
    "df.index.name=\"序号\"\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 研究方向与思路"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 1.创始人有几家独角兽企业？创始人的拥有的独角兽市值？\n",
    "* 2.独角兽企业的地区分布有没有什么特点？欧盟地区和亚太地区的独角兽企业的数量、估值、行业如何？哪些行业拥有的市值较高？与什么因素有关系？\n",
    "* 3.中国的独角兽企业情况是什么样的？每个城市又是什么样的状况？\n",
    "* 4.中国的哪些行业发展较好？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "      <th>掌门人/创始人(拆)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>序号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "      <td>井贤栋</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "      <td>张一鸣</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "      <td>程维</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Infor</td>\n",
       "      <td>Infor</td>\n",
       "      <td>3500</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>云计算</td>\n",
       "      <td>Jim Schaper</td>\n",
       "      <td>2002</td>\n",
       "      <td>Golden Gate Capital, Koch Equity Development</td>\n",
       "      <td>Jim Schaper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>3400</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>消费品</td>\n",
       "      <td>Adam Bowen, James Monsees, Kevin Burns, Tim Da...</td>\n",
       "      <td>2015</td>\n",
       "      <td>M13, Timothy Davis, Evolution VC Partners, Tig...</td>\n",
       "      <td>Adam Bowen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>264</td>\n",
       "      <td>Zipline International</td>\n",
       "      <td>Zipline International</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>半月湾</td>\n",
       "      <td>物流</td>\n",
       "      <td>Keenan Wyrobek, Keller Rinaudo, Will Hetzler</td>\n",
       "      <td>2014</td>\n",
       "      <td>Sequoia Capital, Visionnaire Ventures, Katalys...</td>\n",
       "      <td>Will Hetzler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>264</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>Ian Siegel, Joe Edmonds, Ward Poulos, Willis Redd</td>\n",
       "      <td>2010</td>\n",
       "      <td>IVP (Institutional Venture Partners)</td>\n",
       "      <td>Ian Siegel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>264</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>Ian Siegel, Joe Edmonds, Ward Poulos, Willis Redd</td>\n",
       "      <td>2010</td>\n",
       "      <td>IVP (Institutional Venture Partners)</td>\n",
       "      <td>Joe Edmonds</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>264</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>Ian Siegel, Joe Edmonds, Ward Poulos, Willis Redd</td>\n",
       "      <td>2010</td>\n",
       "      <td>IVP (Institutional Venture Partners)</td>\n",
       "      <td>Ward Poulos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>264</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>Ian Siegel, Joe Edmonds, Ward Poulos, Willis Redd</td>\n",
       "      <td>2010</td>\n",
       "      <td>IVP (Institutional Venture Partners)</td>\n",
       "      <td>Willis Redd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>911 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      排名                   企业名称           Company Name  估值（亿人民币）  国家   城市  \\\n",
       "序号                                                                          \n",
       "0      1                   蚂蚁金服          Ant Financial     10000  中国   杭州   \n",
       "1      2                   字节跳动              Bytedance      5000  中国   北京   \n",
       "2      3                   滴滴出行           Didi Chuxing      3600  中国   北京   \n",
       "3      4                  Infor                  Infor      3500  美国   纽约   \n",
       "4      5              JUUL Labs              JUUL Labs      3400  美国  旧金山   \n",
       "..   ...                    ...                    ...       ...  ..  ...   \n",
       "492  264  Zipline International  Zipline International        70  美国  半月湾   \n",
       "493  264           ZipRecruiter           ZipRecruiter        70  美国  洛杉矶   \n",
       "493  264           ZipRecruiter           ZipRecruiter        70  美国  洛杉矶   \n",
       "493  264           ZipRecruiter           ZipRecruiter        70  美国  洛杉矶   \n",
       "493  264           ZipRecruiter           ZipRecruiter        70  美国  洛杉矶   \n",
       "\n",
       "        行业                                            掌门人/创始人  成立年份  \\\n",
       "序号                                                                    \n",
       "0     金融科技                                                井贤栋  2014   \n",
       "1    媒体和娱乐                                                张一鸣  2012   \n",
       "2     共享经济                                                 程维  2012   \n",
       "3      云计算                                        Jim Schaper  2002   \n",
       "4      消费品  Adam Bowen, James Monsees, Kevin Burns, Tim Da...  2015   \n",
       "..     ...                                                ...   ...   \n",
       "492     物流       Keenan Wyrobek, Keller Rinaudo, Will Hetzler  2014   \n",
       "493   电子商务  Ian Siegel, Joe Edmonds, Ward Poulos, Willis Redd  2010   \n",
       "493   电子商务  Ian Siegel, Joe Edmonds, Ward Poulos, Willis Redd  2010   \n",
       "493   电子商务  Ian Siegel, Joe Edmonds, Ward Poulos, Willis Redd  2010   \n",
       "493   电子商务  Ian Siegel, Joe Edmonds, Ward Poulos, Willis Redd  2010   \n",
       "\n",
       "                                                部分投资机构     掌门人/创始人(拆)  \n",
       "序号                                                                     \n",
       "0                                       春华资本、中投海外、红杉资本            井贤栋  \n",
       "1                                  红杉资本、海纳亚洲、纪源资本、启明创投            张一鸣  \n",
       "2                               腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本             程维  \n",
       "3         Golden Gate Capital, Koch Equity Development    Jim Schaper  \n",
       "4    M13, Timothy Davis, Evolution VC Partners, Tig...     Adam Bowen  \n",
       "..                                                 ...            ...  \n",
       "492  Sequoia Capital, Visionnaire Ventures, Katalys...   Will Hetzler  \n",
       "493               IVP (Institutional Venture Partners)     Ian Siegel  \n",
       "493               IVP (Institutional Venture Partners)    Joe Edmonds  \n",
       "493               IVP (Institutional Venture Partners)    Ward Poulos  \n",
       "493               IVP (Institutional Venture Partners)    Willis Redd  \n",
       "\n",
       "[911 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#创始人清洗\n",
    "df.index.name=\"序号\"\n",
    "df_创始人拆分 =pd.merge(df,\\\n",
    "                     df['掌门人/创始人'].str.split(',', expand=True).stack().reset_index(level=1,drop=True).rename('掌门人/创始人(拆)'),\\\n",
    "                     on=\"序号\")\n",
    "df_创始人拆分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>企业名称</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>掌门人/创始人(拆)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>井贤栋</th>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>张一鸣</th>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>程维</th>\n",
       "      <td>3600</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Bowen</th>\n",
       "      <td>3550</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jim Schaper</th>\n",
       "      <td>3500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James Ramsey</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jason Kelly</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jeff Chapin</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jennifer Hyman</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>龙沛智</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 估值（亿人民币）  企业名称\n",
       "掌门人/创始人(拆)                     \n",
       "井贤栋                 10000     1\n",
       "张一鸣                  5000     1\n",
       "程维                   3600     1\n",
       "Adam Bowen           3550     2\n",
       "Jim Schaper          3500     1\n",
       "...                   ...   ...\n",
       " James Ramsey          70     1\n",
       " Jason Kelly           70     1\n",
       " Jeff Chapin           70     1\n",
       " Jennifer Hyman        70     1\n",
       "龙沛智                    70     1\n",
       "\n",
       "[899 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_创始人拆分[[\"企业名称\",\"估值（亿人民币）\",\"掌门人/创始人(拆)\"]]\\\n",
    "                .groupby([\"掌门人/创始人(拆)\"])\\\n",
    "                .agg({\"估值（亿人民币）\":\"sum\",\"企业名称\":\"count\"})\\\n",
    "                .sort_values(\"估值（亿人民币）\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#投资机构清洗\n",
    "df_部分投资机构拆分 = pd.merge(df,\\\n",
    "                            df['部分投资机构'].str.split('[,,、]', expand=True)\\\n",
    "                            .stack().reset_index(level=1, drop=True).rename('部分投资机构(拆)'),\\\n",
    "                            on=\"序号\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "      <th>部分投资机构(拆)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>序号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "      <td>春华资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "      <td>中投海外</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "      <td>红杉资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "      <td>红杉资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "      <td>海纳亚洲</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>264</td>\n",
       "      <td>转转</td>\n",
       "      <td>Zhuanzhuan</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>姚劲波</td>\n",
       "      <td>2015</td>\n",
       "      <td>腾讯</td>\n",
       "      <td>腾讯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>264</td>\n",
       "      <td>Zipline International</td>\n",
       "      <td>Zipline International</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>半月湾</td>\n",
       "      <td>物流</td>\n",
       "      <td>Keenan Wyrobek, Keller Rinaudo, Will Hetzler</td>\n",
       "      <td>2014</td>\n",
       "      <td>Sequoia Capital, Visionnaire Ventures, Katalys...</td>\n",
       "      <td>Sequoia Capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>264</td>\n",
       "      <td>Zipline International</td>\n",
       "      <td>Zipline International</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>半月湾</td>\n",
       "      <td>物流</td>\n",
       "      <td>Keenan Wyrobek, Keller Rinaudo, Will Hetzler</td>\n",
       "      <td>2014</td>\n",
       "      <td>Sequoia Capital, Visionnaire Ventures, Katalys...</td>\n",
       "      <td>Visionnaire Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>264</td>\n",
       "      <td>Zipline International</td>\n",
       "      <td>Zipline International</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>半月湾</td>\n",
       "      <td>物流</td>\n",
       "      <td>Keenan Wyrobek, Keller Rinaudo, Will Hetzler</td>\n",
       "      <td>2014</td>\n",
       "      <td>Sequoia Capital, Visionnaire Ventures, Katalys...</td>\n",
       "      <td>Katalyst.Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>264</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "      <td>70</td>\n",
       "      <td>美国</td>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>Ian Siegel, Joe Edmonds, Ward Poulos, Willis Redd</td>\n",
       "      <td>2010</td>\n",
       "      <td>IVP (Institutional Venture Partners)</td>\n",
       "      <td>IVP (Institutional Venture Partners)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1835 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      排名                   企业名称           Company Name  估值（亿人民币）  国家   城市  \\\n",
       "序号                                                                          \n",
       "0      1                   蚂蚁金服          Ant Financial     10000  中国   杭州   \n",
       "0      1                   蚂蚁金服          Ant Financial     10000  中国   杭州   \n",
       "0      1                   蚂蚁金服          Ant Financial     10000  中国   杭州   \n",
       "1      2                   字节跳动              Bytedance      5000  中国   北京   \n",
       "1      2                   字节跳动              Bytedance      5000  中国   北京   \n",
       "..   ...                    ...                    ...       ...  ..  ...   \n",
       "491  264                     转转             Zhuanzhuan        70  中国   北京   \n",
       "492  264  Zipline International  Zipline International        70  美国  半月湾   \n",
       "492  264  Zipline International  Zipline International        70  美国  半月湾   \n",
       "492  264  Zipline International  Zipline International        70  美国  半月湾   \n",
       "493  264           ZipRecruiter           ZipRecruiter        70  美国  洛杉矶   \n",
       "\n",
       "        行业                                            掌门人/创始人  成立年份  \\\n",
       "序号                                                                    \n",
       "0     金融科技                                                井贤栋  2014   \n",
       "0     金融科技                                                井贤栋  2014   \n",
       "0     金融科技                                                井贤栋  2014   \n",
       "1    媒体和娱乐                                                张一鸣  2012   \n",
       "1    媒体和娱乐                                                张一鸣  2012   \n",
       "..     ...                                                ...   ...   \n",
       "491   电子商务                                                姚劲波  2015   \n",
       "492     物流       Keenan Wyrobek, Keller Rinaudo, Will Hetzler  2014   \n",
       "492     物流       Keenan Wyrobek, Keller Rinaudo, Will Hetzler  2014   \n",
       "492     物流       Keenan Wyrobek, Keller Rinaudo, Will Hetzler  2014   \n",
       "493   电子商务  Ian Siegel, Joe Edmonds, Ward Poulos, Willis Redd  2010   \n",
       "\n",
       "                                                部分投资机构  \\\n",
       "序号                                                       \n",
       "0                                       春华资本、中投海外、红杉资本   \n",
       "0                                       春华资本、中投海外、红杉资本   \n",
       "0                                       春华资本、中投海外、红杉资本   \n",
       "1                                  红杉资本、海纳亚洲、纪源资本、启明创投   \n",
       "1                                  红杉资本、海纳亚洲、纪源资本、启明创投   \n",
       "..                                                 ...   \n",
       "491                                                 腾讯   \n",
       "492  Sequoia Capital, Visionnaire Ventures, Katalys...   \n",
       "492  Sequoia Capital, Visionnaire Ventures, Katalys...   \n",
       "492  Sequoia Capital, Visionnaire Ventures, Katalys...   \n",
       "493               IVP (Institutional Venture Partners)   \n",
       "\n",
       "                                部分投资机构(拆)  \n",
       "序号                                         \n",
       "0                                    春华资本  \n",
       "0                                    中投海外  \n",
       "0                                    红杉资本  \n",
       "1                                    红杉资本  \n",
       "1                                    海纳亚洲  \n",
       "..                                    ...  \n",
       "491                                    腾讯  \n",
       "492                       Sequoia Capital  \n",
       "492                  Visionnaire Ventures  \n",
       "492                     Katalyst.Ventures  \n",
       "493  IVP (Institutional Venture Partners)  \n",
       "\n",
       "[1835 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_部分投资机构拆分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>企业数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>部分投资机构(拆)</th>\n",
       "      <th>成立年份</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">红杉资本</th>\n",
       "      <th>2014</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>腾讯</th>\n",
       "      <th>2014</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDG</th>\n",
       "      <th>2012</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>红杉资本</th>\n",
       "      <th>2011</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>启明创投</th>\n",
       "      <th>2012</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>顺为资本</th>\n",
       "      <th>2014</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tiger Global Management</th>\n",
       "      <th>2012</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>腾讯</th>\n",
       "      <th>2011</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DST Global</th>\n",
       "      <th>2013</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>纪源资本</th>\n",
       "      <th>2014</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDG</th>\n",
       "      <th>2014</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>腾讯</th>\n",
       "      <th>2016</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>真格基金</th>\n",
       "      <th>2012</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insight Partners</th>\n",
       "      <th>2012</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sequoia Capital</th>\n",
       "      <th>2010</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>晨兴资本</th>\n",
       "      <th>2012</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tiger Global Management</th>\n",
       "      <th>2011</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               企业数量\n",
       "部分投资机构(拆)                成立年份      \n",
       "红杉资本                     2014    10\n",
       "                         2012     8\n",
       "                         2015     7\n",
       "                         2013     7\n",
       "腾讯                       2014     7\n",
       "IDG                      2012     6\n",
       "红杉资本                     2011     6\n",
       "启明创投                     2012     5\n",
       "顺为资本                     2014     5\n",
       " Tiger Global Management 2012     5\n",
       "腾讯                       2011     5\n",
       " DST Global              2013     5\n",
       "纪源资本                     2014     5\n",
       "IDG                      2014     5\n",
       "腾讯                       2016     4\n",
       "真格基金                     2012     4\n",
       " Insight Partners        2012     4\n",
       " Sequoia Capital         2010     4\n",
       "晨兴资本                     2012     4\n",
       " Tiger Global Management 2011     4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_部分投资机构拆分[['企业名称','部分投资机构(拆)','成立年份']]\\\n",
    "                .groupby(['部分投资机构(拆)','成立年份'])\\\n",
    "                .agg(企业数量=('企业名称','count'))\\\n",
    "                .sort_values('企业数量',ascending=False)\\\n",
    "                .head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创始人分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>掌门人/创始人(拆)</th>\n",
       "      <th>企业名称</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>序号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>井贤栋</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>张一鸣</td>\n",
       "      <td>字节跳动</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>程维</td>\n",
       "      <td>滴滴出行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jim Schaper</td>\n",
       "      <td>Infor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adam Bowen</td>\n",
       "      <td>JUUL Labs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>Will Hetzler</td>\n",
       "      <td>Zipline International</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Ian Siegel</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Joe Edmonds</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Ward Poulos</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Willis Redd</td>\n",
       "      <td>ZipRecruiter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>911 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        掌门人/创始人(拆)                   企业名称\n",
       "序号                                       \n",
       "0              井贤栋                   蚂蚁金服\n",
       "1              张一鸣                   字节跳动\n",
       "2               程维                   滴滴出行\n",
       "3      Jim Schaper                  Infor\n",
       "4       Adam Bowen              JUUL Labs\n",
       "..             ...                    ...\n",
       "492   Will Hetzler  Zipline International\n",
       "493     Ian Siegel           ZipRecruiter\n",
       "493    Joe Edmonds           ZipRecruiter\n",
       "493    Ward Poulos           ZipRecruiter\n",
       "493    Willis Redd           ZipRecruiter\n",
       "\n",
       "[911 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "创始人=df_创始人拆分[['掌门人/创始人(拆)','企业名称']]\n",
    "创始人"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 创始人拥有的企业数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>企业数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>掌门人/创始人(拆)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>张勇</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomer Bar Zeev</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>左晖</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vijay Shekhar Sharma</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solomon Hykes</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stephen Fitzpatrick</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steven Barlow</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stu Sjouwerman</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>龙沛智</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      企业数量\n",
       "掌门人/创始人(拆)                \n",
       "张勇                       3\n",
       "-                        3\n",
       " Tomer Bar Zeev          2\n",
       "左晖                       2\n",
       "Vijay Shekhar Sharma     2\n",
       "...                    ...\n",
       "Solomon Hykes            1\n",
       "Stephen Fitzpatrick      1\n",
       "Steven Barlow            1\n",
       "Stu Sjouwerman           1\n",
       "龙沛智                      1\n",
       "\n",
       "[899 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "创始人=创始人.groupby(['掌门人/创始人(拆)'])\\\n",
    "                .agg({'企业名称':'count'})\\\n",
    "                .sort_values('企业名称',ascending=False)\n",
    "创始人.rename(columns={ '企业名称':'企业数量'}, inplace = True)\n",
    "创始人"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 由以上分析可知，张勇拥有的企业数量最多。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 创始人拥有的独角兽市值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>掌门人/创始人(拆)</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>序号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>井贤栋</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>张一鸣</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>程维</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jim Schaper</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adam Bowen</td>\n",
       "      <td>3400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>Will Hetzler</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Ian Siegel</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Joe Edmonds</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Ward Poulos</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Willis Redd</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>911 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        掌门人/创始人(拆)  估值（亿人民币）\n",
       "序号                          \n",
       "0              井贤栋     10000\n",
       "1              张一鸣      5000\n",
       "2               程维      3600\n",
       "3      Jim Schaper      3500\n",
       "4       Adam Bowen      3400\n",
       "..             ...       ...\n",
       "492   Will Hetzler        70\n",
       "493     Ian Siegel        70\n",
       "493    Joe Edmonds        70\n",
       "493    Ward Poulos        70\n",
       "493    Willis Redd        70\n",
       "\n",
       "[911 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "创始人_市值=df_创始人拆分[['掌门人/创始人(拆)','估值（亿人民币）']]\n",
    "创始人_市值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "创始人_市值=创始人_市值.groupby(['掌门人/创始人(拆)'])\\\n",
    ".agg({'估值（亿人民币）':'sum'}) \\\n",
    ".sort_values(['估值（亿人民币）'],ascending=False)\n",
    "创始人_市值=创始人_市值.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "创始人估值=pd.DataFrame({\"创始人\":创始人_市值[\"掌门人/创始人(拆)\"]\n",
    "                              ,\"估值\":创始人_市值[\"估值（亿人民币）\"]})\n",
    "创始人估值分析=创始人估值.sort_values(by=[\"估值\"],ascending=False)[:20]\n",
    "plt.figure(figsize=(12,8))\n",
    "ax=sns.catplot(x=\"创始人\",\n",
    "            y=\"估值\",\n",
    "            data=创始人估值分析,\n",
    "            kind=\"bar\",aspect=2.5)\n",
    "plt.xlabel(xlabel=\"创始人\",fontsize=14)     # 设置x轴\n",
    "plt.ylabel(ylabel=\"估值\",fontsize=14)     # 设置y轴\n",
    "ax.set_xticklabels(rotation=45,fontsize=14)     # 设置大小\n",
    "ax.set_yticklabels(fontsize=14)\n",
    "plt.title('中国独角兽企业估值最高的20个创始人',fontsize=16,pad=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 由上图可知，中国独角兽企业估值最高的创始人是井贤栋，其次是张一鸣。\n",
    "* 在前20名中，只有7位是中国人，其他都为外国人。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 地区分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 欧盟地区"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['中国', '美国', '新加坡', '印度尼西亚', '印度', '韩国', '瑞士', '英国', '德国', '瑞典',\n",
       "       '巴西', '澳大利亚', '马耳他', '法国', '以色列', '爱尔兰', '日本', '阿根廷', '爱沙尼亚',\n",
       "       '西班牙', '卢森堡', '芬兰', '哥伦比亚', '菲律宾'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"国家\"].unique()\n",
    "# unique,以数组形式（numpy.ndarray）返回列的所有唯一值（特征的所有唯一值）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>序号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>50</td>\n",
       "      <td>The Hut Group</td>\n",
       "      <td>The Hut Group</td>\n",
       "      <td>350</td>\n",
       "      <td>英国</td>\n",
       "      <td>曼彻斯特</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>John Gallemore, Matthew Moulding</td>\n",
       "      <td>2004</td>\n",
       "      <td>Artemis, Lewis Trust Group, Kohlberg Kravis Ro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>Auto1 Group</td>\n",
       "      <td>Auto1 Group</td>\n",
       "      <td>300</td>\n",
       "      <td>德国</td>\n",
       "      <td>柏林</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>Christian Bertermann, Hakan Koc</td>\n",
       "      <td>2012</td>\n",
       "      <td>DN Capital, Piton Capital, DST Global, Princev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>57</td>\n",
       "      <td>Klarna</td>\n",
       "      <td>Klarna</td>\n",
       "      <td>300</td>\n",
       "      <td>瑞典</td>\n",
       "      <td>斯德哥尔摩</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>Niklas Adalberth, Sebastian Siemiatkowski, Vic...</td>\n",
       "      <td>2005</td>\n",
       "      <td>Investment AB Öresund, Sequoia Capital, Genera...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>57</td>\n",
       "      <td>TransferWise</td>\n",
       "      <td>TransferWise</td>\n",
       "      <td>300</td>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>Kristo Kaarmann, Taavet Hinrikus</td>\n",
       "      <td>2011</td>\n",
       "      <td>Seedcamp, IA Ventures , Valar Ventures, Index ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>83</td>\n",
       "      <td>Greensill</td>\n",
       "      <td>Greensill</td>\n",
       "      <td>250</td>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>Jason Austin, Lex Greensill</td>\n",
       "      <td>2011</td>\n",
       "      <td>General Atlantic, SoftBank Investment Advisers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>84</td>\n",
       "      <td>Monzo</td>\n",
       "      <td>Monzo</td>\n",
       "      <td>200</td>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>Gary Dolman, Jason Bates, Jonas Huckestein, Pa...</td>\n",
       "      <td>2015</td>\n",
       "      <td>Y Combinator, Passion Capital, Thrive Capital,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>84</td>\n",
       "      <td>N26</td>\n",
       "      <td>N26</td>\n",
       "      <td>200</td>\n",
       "      <td>德国</td>\n",
       "      <td>柏林</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>Maximilian Tayenthal, Valentin Stalf</td>\n",
       "      <td>2013</td>\n",
       "      <td>Earlybird Venture Capital, Valar Ventures, Hor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>84</td>\n",
       "      <td>OakNorth</td>\n",
       "      <td>OakNorth</td>\n",
       "      <td>200</td>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>Joel Perlman, Rishi Khosla</td>\n",
       "      <td>2013</td>\n",
       "      <td>EDBI, SoftBank Investment Advisers, NIBC Bank ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>138</td>\n",
       "      <td>BenevolentAI</td>\n",
       "      <td>BenevolentAI</td>\n",
       "      <td>150</td>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>Brent Gutekunst, Ivan Griffin, Ken Mulvany, Mi...</td>\n",
       "      <td>2013</td>\n",
       "      <td>Woodford Investment Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>138</td>\n",
       "      <td>Binance</td>\n",
       "      <td>Binance</td>\n",
       "      <td>150</td>\n",
       "      <td>马耳他</td>\n",
       "      <td>-</td>\n",
       "      <td>区块链</td>\n",
       "      <td>赵长鹏、何一</td>\n",
       "      <td>2017</td>\n",
       "      <td>Vertex Ventures, Black Hole Capital, Funcity C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>138</td>\n",
       "      <td>BlaBlaCar</td>\n",
       "      <td>BlaBlaCar</td>\n",
       "      <td>150</td>\n",
       "      <td>法国</td>\n",
       "      <td>巴黎</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>Francis Nappez, Frédéric Mazzella, Nicolas Bru...</td>\n",
       "      <td>2006</td>\n",
       "      <td>Accel, Index Ventures, Insight Partners, Barin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>138</td>\n",
       "      <td>Checkout.com</td>\n",
       "      <td>Checkout.com</td>\n",
       "      <td>150</td>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>Guillaume Pousaz</td>\n",
       "      <td>2012</td>\n",
       "      <td>DST Global , Insight Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>138</td>\n",
       "      <td>CureVac</td>\n",
       "      <td>CureVac</td>\n",
       "      <td>150</td>\n",
       "      <td>德国</td>\n",
       "      <td>巴登符腾堡州</td>\n",
       "      <td>生命科学</td>\n",
       "      <td>Ingmar Hoerr</td>\n",
       "      <td>2000</td>\n",
       "      <td>DH Capital, Dievini Hopp Biotech Holding, OH B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>138</td>\n",
       "      <td>Deliveroo</td>\n",
       "      <td>Deliveroo</td>\n",
       "      <td>150</td>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>物流</td>\n",
       "      <td>Greg Orlowski, William Shu</td>\n",
       "      <td>2012</td>\n",
       "      <td>Bridgepoint, Fidelity Management and Research ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>138</td>\n",
       "      <td>FlixBus</td>\n",
       "      <td>FlixBus</td>\n",
       "      <td>150</td>\n",
       "      <td>德国</td>\n",
       "      <td>慕尼黑</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>André Schwämmlein, Daniel Krauss, Jochen Engert</td>\n",
       "      <td>2011</td>\n",
       "      <td>Permira, TCV, Silver Lake Partners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>138</td>\n",
       "      <td>Graphcore</td>\n",
       "      <td>Graphcore</td>\n",
       "      <td>150</td>\n",
       "      <td>英国</td>\n",
       "      <td>布里斯托尔</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>Nigel Toon, Simon Knowles</td>\n",
       "      <td>2016</td>\n",
       "      <td>Amadeus Capital Partners, Atomico, Sequoia Cap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>138</td>\n",
       "      <td>Improbable</td>\n",
       "      <td>Improbable</td>\n",
       "      <td>150</td>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>游戏</td>\n",
       "      <td>Herman Narula, Peter Lipka, Rob Whitehead</td>\n",
       "      <td>2012</td>\n",
       "      <td>SoftBank Investment Advisers, NetEase, Amadeus...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>138</td>\n",
       "      <td>Kaseya</td>\n",
       "      <td>Kaseya</td>\n",
       "      <td>150</td>\n",
       "      <td>爱尔兰</td>\n",
       "      <td>都柏林</td>\n",
       "      <td>云计算</td>\n",
       "      <td>Gerald Blackie</td>\n",
       "      <td>2000</td>\n",
       "      <td>TPG, Ireland Strategic Investment Fund, Insigh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>138</td>\n",
       "      <td>Northvolt</td>\n",
       "      <td>Northvolt</td>\n",
       "      <td>150</td>\n",
       "      <td>瑞典</td>\n",
       "      <td>斯德哥尔摩</td>\n",
       "      <td>新能源</td>\n",
       "      <td>Paolo Cerruti, Peter Carlsson</td>\n",
       "      <td>2016</td>\n",
       "      <td>Volkswagen Group, Goldman Sachs, Seimens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>138</td>\n",
       "      <td>Oxford Nanopore Technologies</td>\n",
       "      <td>Oxford Nanopore Technologies</td>\n",
       "      <td>150</td>\n",
       "      <td>英国</td>\n",
       "      <td>牛津</td>\n",
       "      <td>生命科学</td>\n",
       "      <td>Gordon Sanghera, Hagan Bayley</td>\n",
       "      <td>2005</td>\n",
       "      <td>Amgen, IP Group Plc, Woodford Investment Manag...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>138</td>\n",
       "      <td>Revolut</td>\n",
       "      <td>Revolut</td>\n",
       "      <td>150</td>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>Nikolay Storonsky, Vlad Yatsenko</td>\n",
       "      <td>2015</td>\n",
       "      <td>Mastercard Start Path, Balderton Capital, Trip...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>264</td>\n",
       "      <td>About You</td>\n",
       "      <td>About You</td>\n",
       "      <td>70</td>\n",
       "      <td>德国</td>\n",
       "      <td>汉堡</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>Sebastian Betz, Tarek Muller</td>\n",
       "      <td>2014</td>\n",
       "      <td>SevenVentures, Bestseller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>264</td>\n",
       "      <td>Bolt</td>\n",
       "      <td>Bolt</td>\n",
       "      <td>70</td>\n",
       "      <td>爱沙尼亚</td>\n",
       "      <td>塔林</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>Markus Villig, Martin Villig, Oliver Leisalu</td>\n",
       "      <td>2013</td>\n",
       "      <td>Didi Chuxing, Daimler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>264</td>\n",
       "      <td>Cabify</td>\n",
       "      <td>Cabify</td>\n",
       "      <td>70</td>\n",
       "      <td>西班牙</td>\n",
       "      <td>马德里</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>Adrian Merino, Francisco Montero, Juan De Anto...</td>\n",
       "      <td>2011</td>\n",
       "      <td>Kevin Laws, Seaya Ventures, Rakuten Capital, I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>264</td>\n",
       "      <td>Deezer</td>\n",
       "      <td>Deezer</td>\n",
       "      <td>70</td>\n",
       "      <td>法国</td>\n",
       "      <td>巴黎</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>Daniel Marhely, Jonathan Benassaya</td>\n",
       "      <td>2006</td>\n",
       "      <td>Access Industries, Idinvest Partners, Kingdom ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>264</td>\n",
       "      <td>Doctolib</td>\n",
       "      <td>Doctolib</td>\n",
       "      <td>70</td>\n",
       "      <td>法国</td>\n",
       "      <td>巴黎</td>\n",
       "      <td>健康科技</td>\n",
       "      <td>Franck Tetzlaff, Ivan Schneider, Jessy Bernal,...</td>\n",
       "      <td>2013</td>\n",
       "      <td>Kerala Ventures, Accel, Bpifrance, Eurazeo, Ge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>264</td>\n",
       "      <td>GetYourGuide</td>\n",
       "      <td>GetYourGuide</td>\n",
       "      <td>70</td>\n",
       "      <td>德国</td>\n",
       "      <td>柏林</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>Jochen Mattes, Johannes Reck, Martin Sieber, P...</td>\n",
       "      <td>2009</td>\n",
       "      <td>PROfounders Capital, Highland Europe , Fritz D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>264</td>\n",
       "      <td>Global Fashion Group</td>\n",
       "      <td>Global Fashion Group</td>\n",
       "      <td>70</td>\n",
       "      <td>卢森堡</td>\n",
       "      <td>卢森堡</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>-</td>\n",
       "      <td>2014</td>\n",
       "      <td>Lewis trust Group. Rocket Internet, Kinnevik AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>264</td>\n",
       "      <td>HMD</td>\n",
       "      <td>HMD</td>\n",
       "      <td>70</td>\n",
       "      <td>芬兰</td>\n",
       "      <td>赫尔辛基</td>\n",
       "      <td>消费品</td>\n",
       "      <td>Jean-Francois Baril</td>\n",
       "      <td>2016</td>\n",
       "      <td>Ginko Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>264</td>\n",
       "      <td>Meero</td>\n",
       "      <td>Meero</td>\n",
       "      <td>70</td>\n",
       "      <td>法国</td>\n",
       "      <td>巴黎</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>Thomas Rebaud</td>\n",
       "      <td>2016</td>\n",
       "      <td>Avenir Growth Capital, Eurazeo Prime Ventures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>264</td>\n",
       "      <td>Omio</td>\n",
       "      <td>Omio</td>\n",
       "      <td>70</td>\n",
       "      <td>德国</td>\n",
       "      <td>柏林</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>Naren Shaam</td>\n",
       "      <td>2012</td>\n",
       "      <td>Goldman Sachs Investment Partners, Kleiner Per...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>264</td>\n",
       "      <td>Ovo Energy</td>\n",
       "      <td>Ovo Energy</td>\n",
       "      <td>70</td>\n",
       "      <td>英国</td>\n",
       "      <td>布里斯托尔</td>\n",
       "      <td>新能源</td>\n",
       "      <td>Stephen Fitzpatrick</td>\n",
       "      <td>2009</td>\n",
       "      <td>Mitsubishi Corp</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      排名                          企业名称                  Company Name  \\\n",
       "序号                                                                     \n",
       "55    50                 The Hut Group                 The Hut Group   \n",
       "56    57                   Auto1 Group                   Auto1 Group   \n",
       "62    57                        Klarna                        Klarna   \n",
       "73    57                  TransferWise                  TransferWise   \n",
       "82    83                     Greensill                     Greensill   \n",
       "107   84                         Monzo                         Monzo   \n",
       "108   84                           N26                           N26   \n",
       "110   84                      OakNorth                      OakNorth   \n",
       "145  138                  BenevolentAI                  BenevolentAI   \n",
       "147  138                       Binance                       Binance   \n",
       "149  138                     BlaBlaCar                     BlaBlaCar   \n",
       "157  138                  Checkout.com                  Checkout.com   \n",
       "160  138                       CureVac                       CureVac   \n",
       "164  138                     Deliveroo                     Deliveroo   \n",
       "169  138                       FlixBus                       FlixBus   \n",
       "172  138                     Graphcore                     Graphcore   \n",
       "177  138                    Improbable                    Improbable   \n",
       "182  138                        Kaseya                        Kaseya   \n",
       "196  138                     Northvolt                     Northvolt   \n",
       "197  138  Oxford Nanopore Technologies  Oxford Nanopore Technologies   \n",
       "207  138                       Revolut                       Revolut   \n",
       "269  264                     About You                     About You   \n",
       "290  264                          Bolt                          Bolt   \n",
       "297  264                        Cabify                        Cabify   \n",
       "317  264                        Deezer                        Deezer   \n",
       "320  264                      Doctolib                      Doctolib   \n",
       "339  264                  GetYourGuide                  GetYourGuide   \n",
       "342  264          Global Fashion Group          Global Fashion Group   \n",
       "349  264                           HMD                           HMD   \n",
       "396  264                         Meero                         Meero   \n",
       "411  264                          Omio                          Omio   \n",
       "418  264                    Ovo Energy                    Ovo Energy   \n",
       "\n",
       "     估值（亿人民币）    国家      城市     行业  \\\n",
       "序号                                   \n",
       "55        350    英国    曼彻斯特   电子商务   \n",
       "56        300    德国      柏林   电子商务   \n",
       "62        300    瑞典   斯德哥尔摩   金融科技   \n",
       "73        300    英国      伦敦   金融科技   \n",
       "82        250    英国      伦敦   金融科技   \n",
       "107       200    英国      伦敦   金融科技   \n",
       "108       200    德国      柏林   金融科技   \n",
       "110       200    英国      伦敦   金融科技   \n",
       "145       150    英国      伦敦   人工智能   \n",
       "147       150   马耳他       -    区块链   \n",
       "149       150    法国      巴黎   共享经济   \n",
       "157       150    英国      伦敦   金融科技   \n",
       "160       150    德国  巴登符腾堡州   生命科学   \n",
       "164       150    英国      伦敦     物流   \n",
       "169       150    德国     慕尼黑   电子商务   \n",
       "172       150    英国   布里斯托尔   人工智能   \n",
       "177       150    英国      伦敦     游戏   \n",
       "182       150   爱尔兰     都柏林    云计算   \n",
       "196       150    瑞典   斯德哥尔摩    新能源   \n",
       "197       150    英国      牛津   生命科学   \n",
       "207       150    英国      伦敦   金融科技   \n",
       "269        70    德国      汉堡   电子商务   \n",
       "290        70  爱沙尼亚      塔林   共享经济   \n",
       "297        70   西班牙     马德里   共享经济   \n",
       "317        70    法国      巴黎  媒体和娱乐   \n",
       "320        70    法国      巴黎   健康科技   \n",
       "339        70    德国      柏林   电子商务   \n",
       "342        70   卢森堡     卢森堡   电子商务   \n",
       "349        70    芬兰    赫尔辛基    消费品   \n",
       "396        70    法国      巴黎   人工智能   \n",
       "411        70    德国      柏林   电子商务   \n",
       "418        70    英国   布里斯托尔    新能源   \n",
       "\n",
       "                                               掌门人/创始人  成立年份  \\\n",
       "序号                                                             \n",
       "55                    John Gallemore, Matthew Moulding  2004   \n",
       "56                     Christian Bertermann, Hakan Koc  2012   \n",
       "62   Niklas Adalberth, Sebastian Siemiatkowski, Vic...  2005   \n",
       "73                    Kristo Kaarmann, Taavet Hinrikus  2011   \n",
       "82                         Jason Austin, Lex Greensill  2011   \n",
       "107  Gary Dolman, Jason Bates, Jonas Huckestein, Pa...  2015   \n",
       "108               Maximilian Tayenthal, Valentin Stalf  2013   \n",
       "110                         Joel Perlman, Rishi Khosla  2013   \n",
       "145  Brent Gutekunst, Ivan Griffin, Ken Mulvany, Mi...  2013   \n",
       "147                                             赵长鹏、何一  2017   \n",
       "149  Francis Nappez, Frédéric Mazzella, Nicolas Bru...  2006   \n",
       "157                                   Guillaume Pousaz  2012   \n",
       "160                                       Ingmar Hoerr  2000   \n",
       "164                         Greg Orlowski, William Shu  2012   \n",
       "169    André Schwämmlein, Daniel Krauss, Jochen Engert  2011   \n",
       "172                          Nigel Toon, Simon Knowles  2016   \n",
       "177          Herman Narula, Peter Lipka, Rob Whitehead  2012   \n",
       "182                                     Gerald Blackie  2000   \n",
       "196                      Paolo Cerruti, Peter Carlsson  2016   \n",
       "197                      Gordon Sanghera, Hagan Bayley  2005   \n",
       "207                   Nikolay Storonsky, Vlad Yatsenko  2015   \n",
       "269                       Sebastian Betz, Tarek Muller  2014   \n",
       "290       Markus Villig, Martin Villig, Oliver Leisalu  2013   \n",
       "297  Adrian Merino, Francisco Montero, Juan De Anto...  2011   \n",
       "317                 Daniel Marhely, Jonathan Benassaya  2006   \n",
       "320  Franck Tetzlaff, Ivan Schneider, Jessy Bernal,...  2013   \n",
       "339  Jochen Mattes, Johannes Reck, Martin Sieber, P...  2009   \n",
       "342                                                  -  2014   \n",
       "349                                Jean-Francois Baril  2016   \n",
       "396                                      Thomas Rebaud  2016   \n",
       "411                                        Naren Shaam  2012   \n",
       "418                                Stephen Fitzpatrick  2009   \n",
       "\n",
       "                                                部分投资机构  \n",
       "序号                                                      \n",
       "55   Artemis, Lewis Trust Group, Kohlberg Kravis Ro...  \n",
       "56   DN Capital, Piton Capital, DST Global, Princev...  \n",
       "62   Investment AB Öresund, Sequoia Capital, Genera...  \n",
       "73   Seedcamp, IA Ventures , Valar Ventures, Index ...  \n",
       "82      General Atlantic, SoftBank Investment Advisers  \n",
       "107  Y Combinator, Passion Capital, Thrive Capital,...  \n",
       "108  Earlybird Venture Capital, Valar Ventures, Hor...  \n",
       "110  EDBI, SoftBank Investment Advisers, NIBC Bank ...  \n",
       "145                     Woodford Investment Management  \n",
       "147  Vertex Ventures, Black Hole Capital, Funcity C...  \n",
       "149  Accel, Index Ventures, Insight Partners, Barin...  \n",
       "157                      DST Global , Insight Partners  \n",
       "160  DH Capital, Dievini Hopp Biotech Holding, OH B...  \n",
       "164  Bridgepoint, Fidelity Management and Research ...  \n",
       "169                 Permira, TCV, Silver Lake Partners  \n",
       "172  Amadeus Capital Partners, Atomico, Sequoia Cap...  \n",
       "177  SoftBank Investment Advisers, NetEase, Amadeus...  \n",
       "182  TPG, Ireland Strategic Investment Fund, Insigh...  \n",
       "196           Volkswagen Group, Goldman Sachs, Seimens  \n",
       "197  Amgen, IP Group Plc, Woodford Investment Manag...  \n",
       "207  Mastercard Start Path, Balderton Capital, Trip...  \n",
       "269                          SevenVentures, Bestseller  \n",
       "290                              Didi Chuxing, Daimler  \n",
       "297  Kevin Laws, Seaya Ventures, Rakuten Capital, I...  \n",
       "317  Access Industries, Idinvest Partners, Kingdom ...  \n",
       "320  Kerala Ventures, Accel, Bpifrance, Eurazeo, Ge...  \n",
       "339  PROfounders Capital, Highland Europe , Fritz D...  \n",
       "342    Lewis trust Group. Rocket Internet, Kinnevik AB  \n",
       "349                                     Ginko Ventures  \n",
       "396      Avenir Growth Capital, Eurazeo Prime Ventures  \n",
       "411  Goldman Sachs Investment Partners, Kleiner Per...  \n",
       "418                                    Mitsubishi Corp  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#筛选欧盟地区\n",
    "欧盟国家=[\"法国\",\"德国\",\"意大利\",\"荷兰\",\"比利时\",\"卢森堡\",\"爱尔兰\",\"英国\",\"丹麦\",\"希腊\",\"西班牙\",\"葡萄牙\",\"芬兰\",\"瑞典\",\"奥地利\",\"爱沙尼亚\",\"拉脱维亚\",\"立陶宛\",\"波兰\",\"捷克\",\"匈牙利\",\"斯洛伐克\",\"斯洛文尼亚\",\"马耳他\",\"塞浦路斯\",\"罗马尼亚\",\"保加利亚\"]\n",
    "欧盟地区=df[df['国家'].isin(欧盟国家)]\n",
    "欧盟地区"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 欧盟地区独角兽企业数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "欧盟地区.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 以上可知，在胡润排行榜中，欧盟地区的独角兽企业数量 有32家"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 从估值、企业数量和行业分布进行统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>企业数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <th>金融科技</th>\n",
       "      <td>1250</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <th>电子商务</th>\n",
       "      <td>660</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">英国</th>\n",
       "      <th>电子商务</th>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <th>金融科技</th>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">德国</th>\n",
       "      <th>金融科技</th>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生命科学</th>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <th>共享经济</th>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <th>云计算</th>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <th>新能源</th>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">英国</th>\n",
       "      <th>游戏</th>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生命科学</th>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <th>区块链</th>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <th>电子商务</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">法国</th>\n",
       "      <th>人工智能</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <th>共享经济</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <th>消费品</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <th>新能源</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <th>共享经济</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            估值（亿人民币）  企业数量\n",
       "国家   行业                   \n",
       "英国   金融科技       1250     6\n",
       "德国   电子商务        660     5\n",
       "英国   电子商务        350     1\n",
       "     人工智能        300     2\n",
       "瑞典   金融科技        300     1\n",
       "德国   金融科技        200     1\n",
       "     生命科学        150     1\n",
       "法国   共享经济        150     1\n",
       "爱尔兰  云计算         150     1\n",
       "瑞典   新能源         150     1\n",
       "英国   游戏          150     1\n",
       "     物流          150     1\n",
       "     生命科学        150     1\n",
       "马耳他  区块链         150     1\n",
       "卢森堡  电子商务         70     1\n",
       "法国   人工智能         70     1\n",
       "     健康科技         70     1\n",
       "     媒体和娱乐        70     1\n",
       "爱沙尼亚 共享经济         70     1\n",
       "芬兰   消费品          70     1\n",
       "英国   新能源          70     1\n",
       "西班牙  共享经济         70     1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从估值、企业数量和行业分布进行统计\n",
    "欧盟地区[['国家','城市','企业名称','估值（亿人民币）','行业']]\\\n",
    ".groupby(['国家','行业'])\\\n",
    ".agg({'估值（亿人民币）':'sum','企业名称':'count'})\\\n",
    ".sort_values(['估值（亿人民币）','企业名称'],ascending=False)\\\n",
    ".rename ( columns = {\"企业名称\":\"企业数量\"} )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 由以上可知，英国的金融行业估值最高，同时企业数量也是最高的。可以看出，英国的金融科技行业的发展前景不错。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>行业</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>企业数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>2011</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>550</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>德国</td>\n",
       "      <td>柏林</td>\n",
       "      <td>2012</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>370</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>2015</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>350</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>英国</td>\n",
       "      <td>曼彻斯特</td>\n",
       "      <td>2004</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>瑞典</td>\n",
       "      <td>斯德哥尔摩</td>\n",
       "      <td>2005</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>德国</td>\n",
       "      <td>柏林</td>\n",
       "      <td>2013</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>2013</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>德国</td>\n",
       "      <td>巴登符腾堡州</td>\n",
       "      <td>2000</td>\n",
       "      <td>生命科学</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>德国</td>\n",
       "      <td>慕尼黑</td>\n",
       "      <td>2011</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>法国</td>\n",
       "      <td>巴黎</td>\n",
       "      <td>2006</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>爱尔兰</td>\n",
       "      <td>都柏林</td>\n",
       "      <td>2000</td>\n",
       "      <td>云计算</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>瑞典</td>\n",
       "      <td>斯德哥尔摩</td>\n",
       "      <td>2016</td>\n",
       "      <td>新能源</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>2012</td>\n",
       "      <td>游戏</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>2012</td>\n",
       "      <td>物流</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>2012</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>2013</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>英国</td>\n",
       "      <td>布里斯托尔</td>\n",
       "      <td>2016</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>英国</td>\n",
       "      <td>牛津</td>\n",
       "      <td>2005</td>\n",
       "      <td>生命科学</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>马耳他</td>\n",
       "      <td>-</td>\n",
       "      <td>2017</td>\n",
       "      <td>区块链</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>卢森堡</td>\n",
       "      <td>卢森堡</td>\n",
       "      <td>2014</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>德国</td>\n",
       "      <td>柏林</td>\n",
       "      <td>2009</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>德国</td>\n",
       "      <td>汉堡</td>\n",
       "      <td>2014</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>法国</td>\n",
       "      <td>巴黎</td>\n",
       "      <td>2006</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>法国</td>\n",
       "      <td>巴黎</td>\n",
       "      <td>2013</td>\n",
       "      <td>健康科技</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>法国</td>\n",
       "      <td>巴黎</td>\n",
       "      <td>2016</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>爱沙尼亚</td>\n",
       "      <td>塔林</td>\n",
       "      <td>2013</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>芬兰</td>\n",
       "      <td>赫尔辛基</td>\n",
       "      <td>2016</td>\n",
       "      <td>消费品</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>英国</td>\n",
       "      <td>布里斯托尔</td>\n",
       "      <td>2009</td>\n",
       "      <td>新能源</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>西班牙</td>\n",
       "      <td>马德里</td>\n",
       "      <td>2011</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      国家      城市  成立年份     行业  估值（亿人民币）  企业数量\n",
       "0     英国      伦敦  2011   金融科技       550     2\n",
       "1     德国      柏林  2012   电子商务       370     2\n",
       "2     英国      伦敦  2015   金融科技       350     2\n",
       "3     英国    曼彻斯特  2004   电子商务       350     1\n",
       "4     瑞典   斯德哥尔摩  2005   金融科技       300     1\n",
       "5     德国      柏林  2013   金融科技       200     1\n",
       "6     英国      伦敦  2013   金融科技       200     1\n",
       "7     德国  巴登符腾堡州  2000   生命科学       150     1\n",
       "8     德国     慕尼黑  2011   电子商务       150     1\n",
       "9     法国      巴黎  2006   共享经济       150     1\n",
       "10   爱尔兰     都柏林  2000    云计算       150     1\n",
       "11    瑞典   斯德哥尔摩  2016    新能源       150     1\n",
       "12    英国      伦敦  2012     游戏       150     1\n",
       "13    英国      伦敦  2012     物流       150     1\n",
       "14    英国      伦敦  2012   金融科技       150     1\n",
       "15    英国      伦敦  2013   人工智能       150     1\n",
       "16    英国   布里斯托尔  2016   人工智能       150     1\n",
       "17    英国      牛津  2005   生命科学       150     1\n",
       "18   马耳他       -  2017    区块链       150     1\n",
       "19   卢森堡     卢森堡  2014   电子商务        70     1\n",
       "20    德国      柏林  2009   电子商务        70     1\n",
       "21    德国      汉堡  2014   电子商务        70     1\n",
       "22    法国      巴黎  2006  媒体和娱乐        70     1\n",
       "23    法国      巴黎  2013   健康科技        70     1\n",
       "24    法国      巴黎  2016   人工智能        70     1\n",
       "25  爱沙尼亚      塔林  2013   共享经济        70     1\n",
       "26    芬兰    赫尔辛基  2016    消费品        70     1\n",
       "27    英国   布里斯托尔  2009    新能源        70     1\n",
       "28   西班牙     马德里  2011   共享经济        70     1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "欧盟数据=欧盟地区[['国家','城市','企业名称','估值（亿人民币）','成立年份','行业']]\\\n",
    ".groupby(['国家','城市','成立年份','行业'])\\\n",
    ".agg({'估值（亿人民币）':\"sum\",'企业名称':'count'})\\\n",
    ".sort_values(['估值（亿人民币）','企业名称'],ascending=False)\\\n",
    ".rename(columns = {\"企业名称\":\"企业数量\"})\\\n",
    ".reset_index()\n",
    "欧盟数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"12\" halign=\"left\">估值（亿人民币）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2009</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>卢森堡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德国</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱尔兰</th>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爱沙尼亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>瑞典</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芬兰</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西班牙</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马耳他</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     估值（亿人民币）                                                              \\\n",
       "成立年份     2000   2004   2005  2006  2009   2011   2012   2013  2014   2015   \n",
       "国家                                                                          \n",
       "卢森堡       NaN    NaN    NaN   NaN   NaN    NaN    NaN    NaN  70.0    NaN   \n",
       "德国      150.0    NaN    NaN   NaN  70.0  150.0  370.0  200.0  70.0    NaN   \n",
       "法国        NaN    NaN    NaN  70.0   NaN    NaN    NaN   70.0   NaN    NaN   \n",
       "爱尔兰     150.0    NaN    NaN   NaN   NaN    NaN    NaN    NaN   NaN    NaN   \n",
       "爱沙尼亚      NaN    NaN    NaN   NaN   NaN    NaN    NaN   70.0   NaN    NaN   \n",
       "瑞典        NaN    NaN  300.0   NaN   NaN    NaN    NaN    NaN   NaN    NaN   \n",
       "芬兰        NaN    NaN    NaN   NaN   NaN    NaN    NaN    NaN   NaN    NaN   \n",
       "英国        NaN  350.0  150.0   NaN  70.0  550.0  150.0  150.0   NaN  350.0   \n",
       "西班牙       NaN    NaN    NaN   NaN   NaN   70.0    NaN    NaN   NaN    NaN   \n",
       "马耳他       NaN    NaN    NaN   NaN   NaN    NaN    NaN    NaN   NaN    NaN   \n",
       "\n",
       "                    \n",
       "成立年份   2016   2017  \n",
       "国家                  \n",
       "卢森堡     NaN    NaN  \n",
       "德国      NaN    NaN  \n",
       "法国     70.0    NaN  \n",
       "爱尔兰     NaN    NaN  \n",
       "爱沙尼亚    NaN    NaN  \n",
       "瑞典    150.0    NaN  \n",
       "芬兰     70.0    NaN  \n",
       "英国    150.0    NaN  \n",
       "西班牙     NaN    NaN  \n",
       "马耳他     NaN  150.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 估值发展趋势\n",
    "欧盟估值数据=欧盟数据.groupby(['国家','成立年份'])\\\n",
    ".agg({\"估值（亿人民币）\":\"sum\",\"估值（亿人民币）\":\"max\",\"估值（亿人民币）\":\"min\"})\\\n",
    ".reset_index()\\\n",
    ".pivot(index=\"国家\",columns=\"成立年份\", values=[\"估值（亿人民币）\"])\n",
    "欧盟估值数据"
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   "source": [
    "欧盟数据.iplot(kind=\"bar\", x=[\"国家\"], y=\"估值（亿人民币）\",color='#cc0000', asFigure=True)"
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  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 由图可知，欧盟地区中，英国的独角兽企业估值最高，其次是德国和瑞典。\n",
    "* 卢森堡、爱沙尼亚、芬兰、西班牙最低。\n",
    "* 根据查询可知欧盟成员国里，德国和英国的经济实力是最强的，因此可以推断，国家的独角兽企业估值与国家经济实力呈正相关关系。"
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   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>行业</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>企业数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>2011</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>550</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>德国</td>\n",
       "      <td>柏林</td>\n",
       "      <td>2012</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>370</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>2015</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>350</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>英国</td>\n",
       "      <td>曼彻斯特</td>\n",
       "      <td>2004</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>瑞典</td>\n",
       "      <td>斯德哥尔摩</td>\n",
       "      <td>2005</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>德国</td>\n",
       "      <td>柏林</td>\n",
       "      <td>2013</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>2013</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>德国</td>\n",
       "      <td>巴登符腾堡州</td>\n",
       "      <td>2000</td>\n",
       "      <td>生命科学</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>德国</td>\n",
       "      <td>慕尼黑</td>\n",
       "      <td>2011</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>法国</td>\n",
       "      <td>巴黎</td>\n",
       "      <td>2006</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>爱尔兰</td>\n",
       "      <td>都柏林</td>\n",
       "      <td>2000</td>\n",
       "      <td>云计算</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>瑞典</td>\n",
       "      <td>斯德哥尔摩</td>\n",
       "      <td>2016</td>\n",
       "      <td>新能源</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>2012</td>\n",
       "      <td>游戏</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>2012</td>\n",
       "      <td>物流</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>2012</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>2013</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>英国</td>\n",
       "      <td>布里斯托尔</td>\n",
       "      <td>2016</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>英国</td>\n",
       "      <td>牛津</td>\n",
       "      <td>2005</td>\n",
       "      <td>生命科学</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>马耳他</td>\n",
       "      <td>-</td>\n",
       "      <td>2017</td>\n",
       "      <td>区块链</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>卢森堡</td>\n",
       "      <td>卢森堡</td>\n",
       "      <td>2014</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>德国</td>\n",
       "      <td>柏林</td>\n",
       "      <td>2009</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>德国</td>\n",
       "      <td>汉堡</td>\n",
       "      <td>2014</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>法国</td>\n",
       "      <td>巴黎</td>\n",
       "      <td>2006</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>法国</td>\n",
       "      <td>巴黎</td>\n",
       "      <td>2013</td>\n",
       "      <td>健康科技</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>法国</td>\n",
       "      <td>巴黎</td>\n",
       "      <td>2016</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>爱沙尼亚</td>\n",
       "      <td>塔林</td>\n",
       "      <td>2013</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>芬兰</td>\n",
       "      <td>赫尔辛基</td>\n",
       "      <td>2016</td>\n",
       "      <td>消费品</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>英国</td>\n",
       "      <td>布里斯托尔</td>\n",
       "      <td>2009</td>\n",
       "      <td>新能源</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>西班牙</td>\n",
       "      <td>马德里</td>\n",
       "      <td>2011</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      国家      城市  成立年份     行业  估值（亿人民币）  企业数量\n",
       "0     英国      伦敦  2011   金融科技       550     2\n",
       "1     德国      柏林  2012   电子商务       370     2\n",
       "2     英国      伦敦  2015   金融科技       350     2\n",
       "3     英国    曼彻斯特  2004   电子商务       350     1\n",
       "4     瑞典   斯德哥尔摩  2005   金融科技       300     1\n",
       "5     德国      柏林  2013   金融科技       200     1\n",
       "6     英国      伦敦  2013   金融科技       200     1\n",
       "7     德国  巴登符腾堡州  2000   生命科学       150     1\n",
       "8     德国     慕尼黑  2011   电子商务       150     1\n",
       "9     法国      巴黎  2006   共享经济       150     1\n",
       "10   爱尔兰     都柏林  2000    云计算       150     1\n",
       "11    瑞典   斯德哥尔摩  2016    新能源       150     1\n",
       "12    英国      伦敦  2012     游戏       150     1\n",
       "13    英国      伦敦  2012     物流       150     1\n",
       "14    英国      伦敦  2012   金融科技       150     1\n",
       "15    英国      伦敦  2013   人工智能       150     1\n",
       "16    英国   布里斯托尔  2016   人工智能       150     1\n",
       "17    英国      牛津  2005   生命科学       150     1\n",
       "18   马耳他       -  2017    区块链       150     1\n",
       "19   卢森堡     卢森堡  2014   电子商务        70     1\n",
       "20    德国      柏林  2009   电子商务        70     1\n",
       "21    德国      汉堡  2014   电子商务        70     1\n",
       "22    法国      巴黎  2006  媒体和娱乐        70     1\n",
       "23    法国      巴黎  2013   健康科技        70     1\n",
       "24    法国      巴黎  2016   人工智能        70     1\n",
       "25  爱沙尼亚      塔林  2013   共享经济        70     1\n",
       "26    芬兰    赫尔辛基  2016    消费品        70     1\n",
       "27    英国   布里斯托尔  2009    新能源        70     1\n",
       "28   西班牙     马德里  2011   共享经济        70     1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "欧盟数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>行业</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>企业数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>金融科技</td>\n",
       "      <td>1750</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>电子商务</td>\n",
       "      <td>1080</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>人工智能</td>\n",
       "      <td>370</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>生命科学</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>共享经济</td>\n",
       "      <td>290</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>新能源</td>\n",
       "      <td>220</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>云计算</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>区块链</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>游戏</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>物流</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>健康科技</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>消费品</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       行业  估值（亿人民币）  企业数量\n",
       "0    金融科技      1750     6\n",
       "1    电子商务      1080     6\n",
       "2    人工智能       370     3\n",
       "3    生命科学       300     2\n",
       "4    共享经济       290     3\n",
       "5     新能源       220     2\n",
       "6     云计算       150     1\n",
       "7     区块链       150     1\n",
       "8      游戏       150     1\n",
       "9      物流       150     1\n",
       "10   健康科技        70     1\n",
       "11  媒体和娱乐        70     1\n",
       "12    消费品        70     1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从行业角度分析\n",
    "欧盟数据[['企业数量','估值（亿人民币）','行业']]\\\n",
    ".groupby(['行业'])\\\n",
    ".agg({'估值（亿人民币）':\"sum\",'企业数量':'count'})\\\n",
    ".sort_values(['估值（亿人民币）','企业数量'],ascending=False)\\\n",
    ".reset_index()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 由以上可知，欧盟地区的金融科技、电子商务行业的估值比较高。\n",
    "* 并且金融科技、电子商务行业的企业数量比较高。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 亚太地区"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>序号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>陆金所</td>\n",
       "      <td>Lufax</td>\n",
       "      <td>2700</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>计葵生</td>\n",
       "      <td>2011</td>\n",
       "      <td>摩根士丹利、中银集团、国泰君安（香港）</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>微众银行</td>\n",
       "      <td>WeBank</td>\n",
       "      <td>1500</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>顾敏</td>\n",
       "      <td>2014</td>\n",
       "      <td>腾讯、华平投资、淡马锡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>264</td>\n",
       "      <td>有利网</td>\n",
       "      <td>Yooli</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>吴逸然</td>\n",
       "      <td>2012</td>\n",
       "      <td>高瓴资本、晨兴资本、软银中国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>264</td>\n",
       "      <td>网易有道</td>\n",
       "      <td>Youdao</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>软件与服务</td>\n",
       "      <td>周枫</td>\n",
       "      <td>2007</td>\n",
       "      <td>君联资本、慕华投资</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>264</td>\n",
       "      <td>云鸟科技</td>\n",
       "      <td>Yunniao</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>物流</td>\n",
       "      <td>韩毅</td>\n",
       "      <td>2014</td>\n",
       "      <td>华平投资、红杉资本、经纬中国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>264</td>\n",
       "      <td>掌门1对1</td>\n",
       "      <td>Zhangmen</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>教育科技</td>\n",
       "      <td>张翼</td>\n",
       "      <td>2014</td>\n",
       "      <td>顺为资本、达晨创投、华平投资</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>264</td>\n",
       "      <td>转转</td>\n",
       "      <td>Zhuanzhuan</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>姚劲波</td>\n",
       "      <td>2015</td>\n",
       "      <td>腾讯</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>222 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      排名   企业名称   Company Name  估值（亿人民币）  国家  城市     行业 掌门人/创始人  成立年份  \\\n",
       "序号                                                                      \n",
       "0      1   蚂蚁金服  Ant Financial     10000  中国  杭州   金融科技     井贤栋  2014   \n",
       "1      2   字节跳动      Bytedance      5000  中国  北京  媒体和娱乐     张一鸣  2012   \n",
       "2      3   滴滴出行   Didi Chuxing      3600  中国  北京   共享经济      程维  2012   \n",
       "6      6    陆金所          Lufax      2700  中国  上海   金融科技     计葵生  2011   \n",
       "10    11   微众银行         WeBank      1500  中国  深圳   金融科技      顾敏  2014   \n",
       "..   ...    ...            ...       ...  ..  ..    ...     ...   ...   \n",
       "486  264    有利网          Yooli        70  中国  北京   金融科技     吴逸然  2012   \n",
       "487  264   网易有道         Youdao        70  中国  北京  软件与服务      周枫  2007   \n",
       "488  264   云鸟科技        Yunniao        70  中国  北京     物流      韩毅  2014   \n",
       "490  264  掌门1对1       Zhangmen        70  中国  上海   教育科技      张翼  2014   \n",
       "491  264     转转     Zhuanzhuan        70  中国  北京   电子商务     姚劲波  2015   \n",
       "\n",
       "                     部分投资机构  \n",
       "序号                           \n",
       "0            春华资本、中投海外、红杉资本  \n",
       "1       红杉资本、海纳亚洲、纪源资本、启明创投  \n",
       "2    腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本  \n",
       "6       摩根士丹利、中银集团、国泰君安（香港）  \n",
       "10              腾讯、华平投资、淡马锡  \n",
       "..                      ...  \n",
       "486          高瓴资本、晨兴资本、软银中国  \n",
       "487               君联资本、慕华投资  \n",
       "488          华平投资、红杉资本、经纬中国  \n",
       "490          顺为资本、达晨创投、华平投资  \n",
       "491                      腾讯  \n",
       "\n",
       "[222 rows x 10 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "亚太国家=[\"中国\",\"日本\",\"韩国\",\"朝鲜\",\"菲律宾\",\"马来西亚\",\"印度尼西亚\",\"新加坡\",\"澳大利亚\",\"新西兰\"]\n",
    "亚太地区=df[df['国家'].isin(亚太国家)]\n",
    "亚太地区"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 亚太地区独角兽企业数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "222"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "亚太地区.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 以上可知，在胡润排行榜中，亚太地区的独角兽企业数量 有222家"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 从估值、企业数量和行业分布进行统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>企业数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>行业</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">中国</th>\n",
       "      <th>金融科技</th>\n",
       "      <td>17960</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>媒体和娱乐</th>\n",
       "      <td>8230</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>共享经济</th>\n",
       "      <td>4740</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电子商务</th>\n",
       "      <td>4220</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>物流</th>\n",
       "      <td>3910</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>2090</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康科技</th>\n",
       "      <td>2060</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新能源汽车</th>\n",
       "      <td>1810</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软件与服务</th>\n",
       "      <td>1460</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器人</th>\n",
       "      <td>1400</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产科技</th>\n",
       "      <td>1340</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区块链</th>\n",
       "      <td>1250</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>教育科技</th>\n",
       "      <td>1190</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <th>共享经济</th>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <th>电子商务</th>\n",
       "      <td>870</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <th>电子商务</th>\n",
       "      <td>740</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <th>大数据</th>\n",
       "      <td>720</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <th>共享经济</th>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">中国</th>\n",
       "      <th>消费品</th>\n",
       "      <td>620</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云计算</th>\n",
       "      <td>460</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生命科学</th>\n",
       "      <td>440</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新零售</th>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <th>电子商务</th>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <th>游戏</th>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <th>网络安全</th>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <th>云计算</th>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <th>物流</th>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <th>人工智能</th>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">中国</th>\n",
       "      <th>新能源</th>\n",
       "      <td>140</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏</th>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <th>区块链</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <th>房地产科技</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <th>金融科技</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             估值（亿人民币）  企业数量\n",
       "国家    行业                   \n",
       "中国    金融科技      17960    22\n",
       "      媒体和娱乐      8230    17\n",
       "      共享经济       4740     8\n",
       "      电子商务       4220    33\n",
       "      物流         3910    16\n",
       "      人工智能       2090    15\n",
       "      健康科技       2060    13\n",
       "      新能源汽车      1810    12\n",
       "      软件与服务      1460    15\n",
       "      机器人        1400     3\n",
       "      房地产科技      1340     7\n",
       "      区块链        1250     4\n",
       "      教育科技       1190    11\n",
       "新加坡   共享经济       1000     1\n",
       "印度尼西亚 电子商务        870     3\n",
       "韩国    电子商务        740     3\n",
       "中国    大数据         720     9\n",
       "印度尼西亚 共享经济        700     1\n",
       "中国    消费品         620     4\n",
       "      云计算         460     5\n",
       "      生命科学        440     4\n",
       "      新零售         360     4\n",
       "新加坡   电子商务        350     1\n",
       "韩国    游戏          350     1\n",
       "中国    网络安全        200     1\n",
       "澳大利亚  云计算         200     1\n",
       "韩国    物流          200     1\n",
       "日本    人工智能        150     1\n",
       "中国    新能源         140     2\n",
       "      游戏          100     1\n",
       "日本    区块链          70     1\n",
       "菲律宾   房地产科技        70     1\n",
       "韩国    金融科技         70     1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从估值、企业数量和行业分布进行统计\n",
    "亚太地区[['国家','城市','企业名称','估值（亿人民币）','行业']]\\\n",
    ".groupby(['国家','行业'])\\\n",
    ".agg({'估值（亿人民币）':'sum','企业名称':'count'})\\\n",
    ".sort_values(['估值（亿人民币）','企业名称'],ascending=False)\\\n",
    ".rename ( columns = {\"企业名称\":\"企业数量\"} )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>行业</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>企业数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>2014</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>2012</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>5140</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>2012</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>3600</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>2011</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>2700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>2014</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>印度尼西亚</td>\n",
       "      <td>雅加达</td>\n",
       "      <td>2011</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>日本</td>\n",
       "      <td>东京</td>\n",
       "      <td>2014</td>\n",
       "      <td>区块链</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>菲律宾</td>\n",
       "      <td>马卡迪</td>\n",
       "      <td>2015</td>\n",
       "      <td>房地产科技</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>韩国</td>\n",
       "      <td>首尔</td>\n",
       "      <td>2005</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>韩国</td>\n",
       "      <td>首尔</td>\n",
       "      <td>2011</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>194 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        国家   城市  成立年份     行业  估值（亿人民币）  企业数量\n",
       "0       中国   杭州  2014   金融科技     10000     1\n",
       "1       中国   北京  2012  媒体和娱乐      5140     3\n",
       "2       中国   北京  2012   共享经济      3600     1\n",
       "3       中国   上海  2011   金融科技      2700     1\n",
       "4       中国   深圳  2014   金融科技      1500     1\n",
       "..     ...  ...   ...    ...       ...   ...\n",
       "189  印度尼西亚  雅加达  2011   电子商务        70     1\n",
       "190     日本   东京  2014    区块链        70     1\n",
       "191    菲律宾  马卡迪  2015  房地产科技        70     1\n",
       "192     韩国   首尔  2005   电子商务        70     1\n",
       "193     韩国   首尔  2011   金融科技        70     1\n",
       "\n",
       "[194 rows x 6 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "亚太数据=亚太地区[['国家','城市','企业名称','估值（亿人民币）','成立年份','行业']]\\\n",
    ".groupby(['国家','城市','成立年份','行业'])\\\n",
    ".agg({'估值（亿人民币）':\"sum\",'企业名称':'count'})\\\n",
    ".sort_values(['估值（亿人民币）','企业名称'],ascending=False)\\\n",
    ".rename(columns = {\"企业名称\":\"企业数量\"})\\\n",
    ".reset_index()\n",
    "亚太数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th colspan=\"20\" halign=\"left\">估值（亿人民币）</th>\n",
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       "    <tr>\n",
       "      <th>成立年份</th>\n",
       "      <th>2000</th>\n",
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       "      <th>2016</th>\n",
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       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>中国</th>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>印度尼西亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>澳大利亚</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>菲律宾</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "      估值（亿人民币）                                                              \\\n",
       "成立年份      2000  2001   2002   2003   2004   2005  2006   2007  2008   2009   \n",
       "国家                                                                           \n",
       "中国        70.0  70.0  200.0  200.0  100.0  100.0  70.0   70.0  70.0   70.0   \n",
       "印度尼西亚      NaN   NaN    NaN    NaN    NaN    NaN   NaN    NaN   NaN  500.0   \n",
       "新加坡        NaN   NaN    NaN    NaN    NaN    NaN   NaN    NaN   NaN    NaN   \n",
       "日本         NaN   NaN    NaN    NaN    NaN    NaN   NaN    NaN   NaN    NaN   \n",
       "澳大利亚       NaN   NaN    NaN    NaN    NaN    NaN   NaN    NaN   NaN    NaN   \n",
       "菲律宾        NaN   NaN    NaN    NaN    NaN    NaN   NaN    NaN   NaN    NaN   \n",
       "韩国         NaN   NaN    NaN    NaN    NaN   70.0   NaN  350.0   NaN    NaN   \n",
       "\n",
       "                                                                      \n",
       "成立年份    2010  2011   2012  2013  2014  2015  2016  2017  2018   2019  \n",
       "国家                                                                    \n",
       "中国      70.0  70.0   70.0  70.0  70.0  70.0  70.0  70.0  70.0  100.0  \n",
       "印度尼西亚  700.0  70.0  300.0   NaN   NaN   NaN   NaN   NaN   NaN    NaN  \n",
       "新加坡      NaN   NaN  350.0   NaN   NaN   NaN   NaN   NaN   NaN    NaN  \n",
       "日本       NaN   NaN    NaN   NaN  70.0   NaN   NaN   NaN   NaN    NaN  \n",
       "澳大利亚     NaN   NaN  200.0   NaN   NaN   NaN   NaN   NaN   NaN    NaN  \n",
       "菲律宾      NaN   NaN    NaN   NaN   NaN  70.0   NaN   NaN   NaN    NaN  \n",
       "韩国     670.0  70.0    NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 估值发展趋势\n",
    "亚太估值数据=亚太数据.groupby(['国家','成立年份'])\\\n",
    ".agg({\"估值（亿人民币）\":\"sum\",\"估值（亿人民币）\":\"max\",\"估值（亿人民币）\":\"min\"})\\\n",
    ".reset_index()\\\n",
    ".pivot(index=\"国家\",columns=\"成立年份\", values=[\"估值（亿人民币）\"])\n",
    "亚太估值数据"
   ]
  },
  {
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "亚太数据.iplot(kind=\"bar\", x=[\"国家\"], y=\"估值（亿人民币）\",color='blue', asFigure=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 由图可知，亚太地区中，中国的独角兽企业估值最高，其次是新加坡和印度尼西亚。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>行业</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>企业数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>金融科技</td>\n",
       "      <td>18030</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>8230</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>共享经济</td>\n",
       "      <td>6440</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>电子商务</td>\n",
       "      <td>6180</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>物流</td>\n",
       "      <td>4110</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>人工智能</td>\n",
       "      <td>2240</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>健康科技</td>\n",
       "      <td>2060</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>新能源汽车</td>\n",
       "      <td>1810</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>软件与服务</td>\n",
       "      <td>1460</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>房地产科技</td>\n",
       "      <td>1410</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>机器人</td>\n",
       "      <td>1400</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>区块链</td>\n",
       "      <td>1320</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>教育科技</td>\n",
       "      <td>1190</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>大数据</td>\n",
       "      <td>720</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>云计算</td>\n",
       "      <td>660</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>消费品</td>\n",
       "      <td>620</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>游戏</td>\n",
       "      <td>450</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>生命科学</td>\n",
       "      <td>440</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>新零售</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>网络安全</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>新能源</td>\n",
       "      <td>140</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       行业  估值（亿人民币）  企业数量\n",
       "0    金融科技     18030    22\n",
       "1   媒体和娱乐      8230    13\n",
       "2    共享经济      6440     9\n",
       "3    电子商务      6180    31\n",
       "4      物流      4110    16\n",
       "5    人工智能      2240    11\n",
       "6    健康科技      2060    13\n",
       "7   新能源汽车      1810    10\n",
       "8   软件与服务      1460    12\n",
       "9   房地产科技      1410     8\n",
       "10    机器人      1400     3\n",
       "11    区块链      1320     4\n",
       "12   教育科技      1190    10\n",
       "13    大数据       720     9\n",
       "14    云计算       660     6\n",
       "15    消费品       620     4\n",
       "16     游戏       450     2\n",
       "17   生命科学       440     4\n",
       "18    新零售       360     4\n",
       "19   网络安全       200     1\n",
       "20    新能源       140     2"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从行业角度分析\n",
    "亚太数据[['企业数量','估值（亿人民币）','行业']]\\\n",
    ".groupby(['行业'])\\\n",
    ".agg({'估值（亿人民币）':\"sum\",'企业数量':'count'})\\\n",
    ".sort_values(['估值（亿人民币）','企业数量'],ascending=False)\\\n",
    ".reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 可以知道，在亚太地区，金融科技行业的估值是最高的，其次是媒体和娱乐、共享经济、电子商务。\n",
    "* 同时电子商务的独角兽企业数量是最高的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 中国分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
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       "    <tr>\n",
       "      <th>序号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>陆金所</td>\n",
       "      <td>Lufax</td>\n",
       "      <td>2700</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>计葵生</td>\n",
       "      <td>2011</td>\n",
       "      <td>摩根士丹利、中银集团、国泰君安（香港）</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>微众银行</td>\n",
       "      <td>WeBank</td>\n",
       "      <td>1500</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>顾敏</td>\n",
       "      <td>2014</td>\n",
       "      <td>腾讯、华平投资、淡马锡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>264</td>\n",
       "      <td>有利网</td>\n",
       "      <td>Yooli</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>吴逸然</td>\n",
       "      <td>2012</td>\n",
       "      <td>高瓴资本、晨兴资本、软银中国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>264</td>\n",
       "      <td>网易有道</td>\n",
       "      <td>Youdao</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>软件与服务</td>\n",
       "      <td>周枫</td>\n",
       "      <td>2007</td>\n",
       "      <td>君联资本、慕华投资</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>264</td>\n",
       "      <td>云鸟科技</td>\n",
       "      <td>Yunniao</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>物流</td>\n",
       "      <td>韩毅</td>\n",
       "      <td>2014</td>\n",
       "      <td>华平投资、红杉资本、经纬中国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>264</td>\n",
       "      <td>掌门1对1</td>\n",
       "      <td>Zhangmen</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>教育科技</td>\n",
       "      <td>张翼</td>\n",
       "      <td>2014</td>\n",
       "      <td>顺为资本、达晨创投、华平投资</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>264</td>\n",
       "      <td>转转</td>\n",
       "      <td>Zhuanzhuan</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>姚劲波</td>\n",
       "      <td>2015</td>\n",
       "      <td>腾讯</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      排名   企业名称   Company Name  估值（亿人民币）  国家  城市     行业 掌门人/创始人  成立年份  \\\n",
       "序号                                                                      \n",
       "0      1   蚂蚁金服  Ant Financial     10000  中国  杭州   金融科技     井贤栋  2014   \n",
       "1      2   字节跳动      Bytedance      5000  中国  北京  媒体和娱乐     张一鸣  2012   \n",
       "2      3   滴滴出行   Didi Chuxing      3600  中国  北京   共享经济      程维  2012   \n",
       "6      6    陆金所          Lufax      2700  中国  上海   金融科技     计葵生  2011   \n",
       "10    11   微众银行         WeBank      1500  中国  深圳   金融科技      顾敏  2014   \n",
       "..   ...    ...            ...       ...  ..  ..    ...     ...   ...   \n",
       "486  264    有利网          Yooli        70  中国  北京   金融科技     吴逸然  2012   \n",
       "487  264   网易有道         Youdao        70  中国  北京  软件与服务      周枫  2007   \n",
       "488  264   云鸟科技        Yunniao        70  中国  北京     物流      韩毅  2014   \n",
       "490  264  掌门1对1       Zhangmen        70  中国  上海   教育科技      张翼  2014   \n",
       "491  264     转转     Zhuanzhuan        70  中国  北京   电子商务     姚劲波  2015   \n",
       "\n",
       "                     部分投资机构  \n",
       "序号                           \n",
       "0            春华资本、中投海外、红杉资本  \n",
       "1       红杉资本、海纳亚洲、纪源资本、启明创投  \n",
       "2    腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本  \n",
       "6       摩根士丹利、中银集团、国泰君安（香港）  \n",
       "10              腾讯、华平投资、淡马锡  \n",
       "..                      ...  \n",
       "486          高瓴资本、晨兴资本、软银中国  \n",
       "487               君联资本、慕华投资  \n",
       "488          华平投资、红杉资本、经纬中国  \n",
       "490          顺为资本、达晨创投、华平投资  \n",
       "491                      腾讯  \n",
       "\n",
       "[206 rows x 10 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_中国=df[df['国家']=='中国']\n",
    "df_中国"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 中国独角兽企业数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_中国.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 在胡润排行榜上，中国共有206个独角兽企业。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 中国独角兽企业在中国城市的累计市值总额情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>企业数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>10290</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>6890</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>4040</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>上海</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>3470</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>2300</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>深圳</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>深圳</td>\n",
       "      <td>房地产科技</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>重庆</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>金华</td>\n",
       "      <td>新能源汽车</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>香港</td>\n",
       "      <td>物流</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 4 columns</p>\n",
       "</div>"
      ],
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       "    城市     行业  估值（亿人民币）  企业数量\n",
       "0   杭州   金融科技     10290     4\n",
       "1   北京  媒体和娱乐      6890     7\n",
       "2   北京   共享经济      4040     5\n",
       "3   上海   金融科技      3470     4\n",
       "4   北京   电子商务      2300    13\n",
       "..  ..    ...       ...   ...\n",
       "82  深圳   人工智能        70     1\n",
       "83  深圳  房地产科技        70     1\n",
       "84  重庆   电子商务        70     1\n",
       "85  金华  新能源汽车        70     1\n",
       "86  香港     物流        70     1\n",
       "\n",
       "[87 rows x 4 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_城市统计=df_中国[['城市','企业名称','估值（亿人民币）','行业']]\\\n",
    "                .groupby(['城市','行业'])\\\n",
    "                .agg({'估值（亿人民币）':'sum','企业名称':'count'})\\\n",
    "                .sort_values(['估值（亿人民币）','企业名称'],ascending=False)\\\n",
    "                .reset_index()\n",
    "df_城市统计.rename(columns={ '企业名称':'企业数量'}, inplace = True)\n",
    "df_城市统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
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       "                            \n",
       "var gd = document.getElementById('b5dc61ca-6978-4ed9-b82f-857a9741011b');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 中国独角兽企业在中国城市的累计市值总额情况\n",
    "df_城市统计.iplot(kind=\"bar\", x=\"城市\", y=\"估值（亿人民币）\",color=\"#238E68\", asFigure=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 由条形堆叠图来看，北京的独角兽企业累计市值最高，超过20k(亿人民币)，第二是杭州，接近15k(亿人民币)。\n",
    "* 上海和深圳则排在第二阶队。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 中国独角兽企业估值情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from pylab import *\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']    # 使图表中文字显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
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   "outputs": [
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       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
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       "      <th>序号</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>陆金所</td>\n",
       "      <td>Lufax</td>\n",
       "      <td>2700</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>计葵生</td>\n",
       "      <td>2011</td>\n",
       "      <td>摩根士丹利、中银集团、国泰君安（香港）</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>微众银行</td>\n",
       "      <td>WeBank</td>\n",
       "      <td>1500</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>顾敏</td>\n",
       "      <td>2014</td>\n",
       "      <td>腾讯、华平投资、淡马锡</td>\n",
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       "      <th>...</th>\n",
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       "      <th>486</th>\n",
       "      <td>264</td>\n",
       "      <td>有利网</td>\n",
       "      <td>Yooli</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>吴逸然</td>\n",
       "      <td>2012</td>\n",
       "      <td>高瓴资本、晨兴资本、软银中国</td>\n",
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       "      <th>487</th>\n",
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       "      <td>北京</td>\n",
       "      <td>软件与服务</td>\n",
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       "      <th>488</th>\n",
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       "      <td>北京</td>\n",
       "      <td>物流</td>\n",
       "      <td>韩毅</td>\n",
       "      <td>2014</td>\n",
       "      <td>华平投资、红杉资本、经纬中国</td>\n",
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       "      <th>490</th>\n",
       "      <td>264</td>\n",
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       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>教育科技</td>\n",
       "      <td>张翼</td>\n",
       "      <td>2014</td>\n",
       "      <td>顺为资本、达晨创投、华平投资</td>\n",
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       "      <th>491</th>\n",
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       "      <td>转转</td>\n",
       "      <td>Zhuanzhuan</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>姚劲波</td>\n",
       "      <td>2015</td>\n",
       "      <td>腾讯</td>\n",
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      ],
      "text/plain": [
       "      排名   企业名称   Company Name  估值（亿人民币）  国家  城市     行业 掌门人/创始人  成立年份  \\\n",
       "序号                                                                      \n",
       "0      1   蚂蚁金服  Ant Financial     10000  中国  杭州   金融科技     井贤栋  2014   \n",
       "1      2   字节跳动      Bytedance      5000  中国  北京  媒体和娱乐     张一鸣  2012   \n",
       "2      3   滴滴出行   Didi Chuxing      3600  中国  北京   共享经济      程维  2012   \n",
       "6      6    陆金所          Lufax      2700  中国  上海   金融科技     计葵生  2011   \n",
       "10    11   微众银行         WeBank      1500  中国  深圳   金融科技      顾敏  2014   \n",
       "..   ...    ...            ...       ...  ..  ..    ...     ...   ...   \n",
       "486  264    有利网          Yooli        70  中国  北京   金融科技     吴逸然  2012   \n",
       "487  264   网易有道         Youdao        70  中国  北京  软件与服务      周枫  2007   \n",
       "488  264   云鸟科技        Yunniao        70  中国  北京     物流      韩毅  2014   \n",
       "490  264  掌门1对1       Zhangmen        70  中国  上海   教育科技      张翼  2014   \n",
       "491  264     转转     Zhuanzhuan        70  中国  北京   电子商务     姚劲波  2015   \n",
       "\n",
       "                     部分投资机构  \n",
       "序号                           \n",
       "0            春华资本、中投海外、红杉资本  \n",
       "1       红杉资本、海纳亚洲、纪源资本、启明创投  \n",
       "2    腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本  \n",
       "6       摩根士丹利、中银集团、国泰君安（香港）  \n",
       "10              腾讯、华平投资、淡马锡  \n",
       "..                      ...  \n",
       "486          高瓴资本、晨兴资本、软银中国  \n",
       "487               君联资本、慕华投资  \n",
       "488          华平投资、红杉资本、经纬中国  \n",
       "490          顺为资本、达晨创投、华平投资  \n",
       "491                      腾讯  \n",
       "\n",
       "[206 rows x 10 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_中国"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
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   },
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "企业估值=pd.DataFrame({\"企业名称\":df_中国[\"企业名称\"]\n",
    "                              ,\"估值\":df_中国[\"估值（亿人民币）\"]})\n",
    "fig=px.pie(企业估值,values=\"估值\",names=\"企业名称\",title=\"中国独角兽企业估值情况\",template=\"seaborn\")\n",
    "fig.update_traces(textposition=\"inside\",textinfo=\"value+percent+label\")#标签位置放在里面，标签信息包含值，百分比，标签\n",
    "fig.show()\n",
    "py.offline.plot(fig, filename=\"中国独角兽企业估值情况.html\",auto_open=False)\n",
    "with open(\"中国独角兽企业估值情况.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "    plot_all1 = \"\".join(f.readlines())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 由图可知,中国的独角兽企业里，蚂蚁金服的估值最高，为10k（亿人民币）。其次是字节跳动、滴滴出行和陆金所。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>企业名称</th>\n",
       "      <th>估值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>序号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>字节跳动</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>陆金所</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>微众银行</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>有利网</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>网易有道</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>云鸟科技</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>掌门1对1</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>转转</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 2 columns</p>\n",
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       "      企业名称     估值\n",
       "序号               \n",
       "0     蚂蚁金服  10000\n",
       "1     字节跳动   5000\n",
       "2     滴滴出行   3600\n",
       "6      陆金所   2700\n",
       "10    微众银行   1500\n",
       "..     ...    ...\n",
       "486    有利网     70\n",
       "487   网易有道     70\n",
       "488   云鸟科技     70\n",
       "490  掌门1对1     70\n",
       "491     转转     70\n",
       "\n",
       "[206 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "企业估值=pd.DataFrame({\"企业名称\":df_中国[\"企业名称\"]\n",
    "                              ,\"估值\":df_中国[\"估值（亿人民币）\"]})\n",
    "企业估值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>企业名称</th>\n",
       "      <th>估值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>序号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>字节跳动</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>陆金所</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>微众银行</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>菜鸟网络</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>京东数科</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>快手</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>大疆</td>\n",
       "      <td>1000</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>京东物流</td>\n",
       "      <td>800</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>比特大陆</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>贝壳找房</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>车好多</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>平安医保科技</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>美菜网</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>金融壹账通</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>苏宁金服</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>神州优车</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>微医</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>商汤科技</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      企业名称     估值\n",
       "序号               \n",
       "0     蚂蚁金服  10000\n",
       "1     字节跳动   5000\n",
       "2     滴滴出行   3600\n",
       "6      陆金所   2700\n",
       "10    微众银行   1500\n",
       "11    菜鸟网络   1300\n",
       "12    京东数科   1300\n",
       "13      快手   1200\n",
       "14      大疆   1000\n",
       "20    京东物流    800\n",
       "19    比特大陆    800\n",
       "24    贝壳找房    600\n",
       "25     车好多    600\n",
       "27  平安医保科技    600\n",
       "34     美菜网    500\n",
       "35   金融壹账通    500\n",
       "37    苏宁金服    500\n",
       "47    神州优车    400\n",
       "48      微医    400\n",
       "46    商汤科技    400"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "企业估值=pd.DataFrame({\"企业名称\":df_中国[\"企业名称\"]\n",
    "                              ,\"估值\":df_中国[\"估值（亿人民币）\"]})\n",
    "企业估值分析=企业估值.sort_values(by=[\"估值\"],ascending=False)[:20]\n",
    "# plt.figure(figsize=(12,8))\n",
    "# ax=sns.catplot(x=\"企业名称\",\n",
    "#             y=\"估值\",\n",
    "#             data=企业估值分析,\n",
    "#             kind=\"bar\",aspect=2.5)\n",
    "# plt.xlabel(xlabel=\"企业名称\",fontsize=14)     # 设置x轴\n",
    "# plt.ylabel(ylabel=\"估值\",fontsize=14)     # 设置y轴\n",
    "# ax.set_xticklabels(rotation=45,fontsize=14)     # 设置大小\n",
    "# ax.set_yticklabels(fontsize=14)\n",
    "# plt.title('中国估值最高的20个企业',fontsize=16,pad=20)\n",
    "# plt.show()\n",
    "\n",
    "# 企业估值=pd.DataFrame({\"企业名称\":df_中国[\"企业名称\"]\n",
    "#                               ,\"估值\":df_中国[\"估值（亿人民币）\"]})\n",
    "\n",
    "企业估值分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "fig=px.bar(企业估值分析,x=\"企业名称\",y=\"估值\",color=\"企业名称\",title=\"中国独角兽企业估值情况\",template=\"seaborn\")\n",
    "#fig.update_traces(textposition=\"inside\",textinfo=\"value+percent+label\")#标签位置放在里面，标签信息包含值，百分比，标签\n",
    "fig.show()"
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  {
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   "source": [
    "#fig = ax.iplot(kind=\"bar\", x=\"行业\", y=\"估值（亿人民币）\", asFigure=True)\n",
    "py.offline.plot(fig,filename=\"中国估值最高的20个企业.html\",auto_open=False)\n",
    "with open(\"中国估值最高的20个企业.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "    plot_all = \"\".join(f.readlines())"
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   "execution_count": 41,
   "metadata": {
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   "source": [
    "企业估值=pd.DataFrame({\"企业名称\":df_中国[\"企业名称\"]\n",
    "                              ,\"估值\":df_中国[\"估值（亿人民币）\"]\n",
    "                              ,\"行业\":df_中国[\"行业\"]})\n",
    "\n",
    "企业估值分析=企业估值.groupby(['行业','企业名称'])\\\n",
    ".agg({'估值':'sum'})\\\n",
    ".sort_values(['估值'],ascending=False)[:50]\n",
    "企业估值分析=企业估值分析.reset_index()\n"
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    "企业估值分析.iplot(kind=\"bar\", x=\"行业\", y=\"估值\",color=\"#238E68\", asFigure=True)"
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    "* 由上图我们可以知道，在中国的独角兽企业市值排名前50里面，金融科技行业、媒体和娱乐、共享经济、物流以及电子商务排名前5。\n",
    "* 在中国，金融科技行业的独角兽企业估值最高。\n"
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    "## 总结"
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    "* 1.在胡润排行榜中，张勇拥有的独角兽企业数量最多。\n",
    "* 2.中国独角兽企业估值最高的创始人是井贤栋，其次是张一鸣。在前20名中，只有7位是中国人，其他都为外国人。\n",
    "* 3.在胡润排行榜中，欧盟地区的独角兽企业数量有32家，亚太有222家。\n",
    "* 4.欧盟地区中，独角兽企业估值最高的是英国，其次是德国和瑞典。其中英国的金融行业估值最高，同时英国的企业数量也是最高的。可以推断，英国的金融科技行业的发展前景不错。\n",
    "* 5.根据查询可知欧盟成员国里，德国和英国的经济实力是最强的，因此可以推断，国家的独角兽企业估值与国家经济实力呈正相关关系。\n",
    "* 6.欧盟地区的金融科技、电子商务行业的估值比较高。并且金融科技、电子商务行业的企业数量比较高。\n",
    "* 7.亚太地区中，中国的独角兽企业估值最高，其次是新加坡和印度尼西亚。并且在亚太地区，金融科技行业的估值是最高的，其次是媒体和娱乐、共享经济、电子商务。同时电子商务的独角兽企业数量是最高的。\n",
    "* 8.在胡润排行榜上，中国共有206个独角兽企业。\n",
    "* 9.在中国城市里，北京的独角兽企业累计市值最高，超过20k(亿人民币)，第二是杭州，接近15k(亿人民币)。上海和深圳则排在第二阶队。\n",
    "* 10.中国的独角兽企业里，蚂蚁金服的估值最高，为10k（亿人民币）。其次是字节跳动、滴滴出行和陆金所。\n",
    "* 11.在中国的独角兽企业市值排名前50里面，金融科技行业、媒体和娱乐、共享经济、物流以及电子商务排名前5。\n",
    "* 12.在中国，金融科技行业的独角兽企业估值最高。"
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